A partition model and strategy based on the Stoer–Wagner algorithm for SaaS multi-tenant data
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Partition technology is the key step to realize the extensional architecture in the cloud and support the data placement on multiple nodes. This paper proposes a multi-tenant data partition model and algorithm for SaaS (Software as a Service) application. It solves the problem that data partitions would produce lots of distributed transactions caused by the existing cloud data management. The management is unconscious of SaaS tenants during the transformation from a single node to multiple nodes in the cloud to obtain the dynamic extension of the system’s scale. With the increase of tenants and data, the single node becomes the bottleneck of the whole system. Fortunately, the scale of the whole system can be expanded by data partition. This paper puts forward a multi-tenant data partition model with three-layer structure: Tenant layer, Relevance, Group layer and Tenant Partition layer. Furthermore, we propose the concepts of Relevance, Relevance Value and Relevance Matrix. The customized tables for one tenant accessed by the same transactions can form a minimum high-relevance granularity based on the Relevance Group algorithm. Then we construct an abstracted graph, where group is the basic unit and transaction accessing is weight. Through the Stoer–Wagner algorithm, the multi-tenant partition with group as granularity is obtained. The partition algorithm proposed in this paper enables the greatest reduction of distributed transactions between partitions while realizing the dynamic extension on multiple nodes for multi-tenant data based on shared storage. Experiments show that the number of distributed transactions is reduced dramatically compared with other data partition techniques. We also prove that the SaaS applications run at high efficiency.
KeywordsSaaS Multi-tenant data Partition Shared schema
This work was partially supported by the National Natural Science Foundation of China (No. 61501276; No. 61502218), Outstanding Young Scientists Foundation Grant of Shandong Province (No. BS2014DX016), Guangzhou Scholars Project (No. 1201561613). Professional Development Support Project for Application-oriented Talents Training in general undergraduate Universities funded by Shandong Provincial Education Department and Shandong Province Finance Bureau in 2015.
Compliance with ethical standards
Conflict of interest
Xiaona Li declares that she has no conflict of interest. Junli Zhao declares that she has no conflict of interest. Yumei Ma declares that she has no conflict of interest. Pingping Wang declares that she has no conflict of interest. Hongyi Sun declares that she has no conflict of interest. Yi Tang declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Agrawal D, El Abbadi A, Antony S, Das S (2010) Data management challenges in cloud computing infrastructures. In: Databases in networked information systems.Springer, pp 1–10Google Scholar
- Agrawal S, Narasayya V, Yang B (2004) Integrating vertical and horizontal partitioning into automated physical database design. In: Proceedings of the 2004 ACM SIGMOD international conference on management of data. ACM, pp 359–370Google Scholar
- Aulbach S, Jacobs D, Kemper A, Seibold M (2009) A comparison of flexible schemas for software as a service. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data. ACM, pp 881–888Google Scholar
- Baker J, Bond C, Corbett JC, Furman JJ, Khorlin A, Larson J, Leon J-M, Li Y, Lloyd A, Yushprakh V (2011) Megastore: providing scalable, highly available storage for interactive services. CIDR 11:223–234Google Scholar
- Campbell DG, Kakivaya G, Ellis N (2010) Extreme scale with full sql language support in microsoft sql azure. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, pp 1021–1024Google Scholar
- Curino C, Jones Zhang Y, Eugene W, Madden S (2010) The case for a database service. New England Database Summit, Relational cloudGoogle Scholar
- Das S, Agrawal D, El Abbadi A (2013) Elastras: an elastic, scalable, and self-managing transactional database for the cloud. ACM Trans Database Syst(TODS) 38(1):5Google Scholar
- Hartung I, Goldschmidt B (2014) Performance analysis of windows azure data storage options. In: Large-scale scientific computing. Springer, pp 499–506Google Scholar
- Li H (2013) Research on key technology in multi-tenant data architecture for saas application. Chongqing University (Doctoral Dissertation), ChongqingGoogle Scholar
- Li J, Wang Y (2006) Universal designated verifier ring signature (proof) without random oracles. In: Emerging directions in embedded and ubiquitous computing. Springer, pp 332–341Google Scholar
- Li J, Zhang F, Wang Y (2006) A new hierarchical id-based cryptosystem and cca-secure pke. In: Emerging directions in embedded and ubiquitous computing. Springer, pp 362–371Google Scholar
- Li J, Kim K, Zhang F, Chen X (2007) Aggregate proxy signature and verifiably encrypted proxy signature. In: Provable security. Springer, pp 208–217Google Scholar
- Li J, Wang Q, Wang C, Cao N, Ren K, Lou W (2010) Fuzzy keyword search over encrypted data in cloud computing. In: INFOCOM, 2010 Proceedings IEEE. IEEE, pp 1–5Google Scholar
- Li X (2015) Research on placement mechanism for saas multi-tenant data. Shandong University (Doctoral Dissertation), JinanGoogle Scholar
- Li X-N, Li Q-Z, Kong L-J, Pang C (2012) Research on multi-tenant data partition mechanism for saas application based on shared schema. J Commun 33(S1):110–120Google Scholar
- Rao J, Zhang C, Megiddo N, Lohman G (2002) Automating physical database design in a parallel database. In: Proceedings of the 2002 ACM SIGMOD international conference on management of data. ACM, pp 558–569Google Scholar
- Schiller O, Cipriani N, Mitschang B (2013) Prorea: live database migration for multi-tenant rdbms with snapshot isolation. In: Proceedings of the 16th international conference on extending database technology. ACM, pp 53–64Google Scholar
- Stoer M, Wagner F (1997) A simple min-cut algorithm. J Acm 44(4):585–591Google Scholar
- Weissman CD, Bobrowski S (2009) The design of the force. com multitenant internet application development platform. In: SIGMOD Conference, pp 889–896Google Scholar
- Zilio DC (1998) Physical database design decision algorithms and concurrent reorganization for parallel database systems. PhD thesis, CiteseerGoogle Scholar